We present a sentiment tagging system which is based on multiple bootstrapping runs on WordNet synsets and glosses using different non-intersecting seed lists of manually annotated words. The system is further enhanced by the addition of a module for partial sense disambiguation of sentiment-bearing adjectives using combinatorial patterns. This (1) enables sentiment annotation at the sense, rather than whole word level, and (2) provides an effective tool for the automatic cleaning of the lists of sentiment-annotated words. The resulting cleaned list of 2907 English sentiment-bearing adjectives achieved a performance comparable to that of human annotation, as evaluated by the agreement rate between two manually annotated lists of sentiment-marked adjectives. The issues of sentiment tag extraction, evaluation and precision/recall tradeoffs are discussed.
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